Search results for: probabilistic classification vector machines

Authors: M. Hisham, S. Cabemaiwai, S. Prasad, T. Dauvakatini, R. Ananthanarayanan

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A solenoid valve is an important part of many fluid systems. Its purpose is to regulate fluid flow in a machine. Due to the crucial role of the solenoid valve and its design intricacy, it is quite expensive to obtain in Fiji and is not manufactured locally. A concern raised by the local health industry is that the housing of the solenoid valve gets damaged when machines are continuously being used and this part of the valve is very costly to replace due to the lack of availability in Fiji and many other South Pacific region countries. This study explores the agile manufacturing of a solenoid coil housing using the Fused Deposition Modeling (FDM) process. An emergy study was carried out to analyze the feasibility and sustainability of producing the part locally after estimating a Unit Emergy Value (or emergy transformity) of 1.27E+05 sej/j for the electricity in Fiji. The total emergy of the process was calculated to be 3.05E+12 sej, of which a majority was sourced from imported services and materials. Renewable emergy sources contributed to just 16.04% of the total emergy. Therefore, the part is suitable to be manufactured in Fiji with a reasonable quality and a cost of $FJ 2.85. However, the loading on the local environment is found to be significant and therefore, alternative raw materials for the filament like recycled PET should be explored or alternative manufacturing processes may be analyzed before committing to fabricating the part using FDM in its analyzed state.

Keywords: emergy analysis, fused deposition modeling, solenoid valve housing, sustainable production

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Authors: Musoke Ivan, Birungi Racheal

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The construction trend is characterized by increased use of modern methods yet traditional methods are cheaper in terms of costs, in addition to the benefits it offers to the construction sector, like providing more jobs that could have been worked with the intensive machines. The purpose of this research was to assess the influence of using Traditional methods of construction (TMC) on the costs and quality of building structures and determine the different ways. Traditional methods of construction (TMC) can be applicable and integrated into the construction trend, and propose ways how this can be a success. The study adopted a quantitative method approach targeting various construction professionals like Architects, Quantity surveyors, Engineers, and Construction Managers. Questionnaires and analyses of literature were used to obtain research data and findings. Simple random sampling was used to select 40 construction professionals to which questionnaires were administered. The data was then analyzed using Microsoft Excel. The findings of the research indicate that Traditional methods of construction (TMCs) in Uganda are cheaper in terms of costs, but the quality is still low. This is attributed to a lack of skilled labour and efficient supervision while undertaking tasks leading to low quality. The study identifies strategies that would improve Traditional methods of construction (TMC), which include the employment of skilled manpower and effective supervision. It also identifies the need by stakeholders like the government, clients, and professionals to appreciate Traditional methods of construction (TMCs) and allow for a levelled ground for Traditional Methods of Construction and Modern methods of construction (MMCs).

Keywords: traditional methods of construction, integration, cost, quality

Procedia PDF Downloads 61

Authors: Rajamani Doraiswami, Lahouari Cheded

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Fault diagnosis of Linear Parameter-Varying (LPV) system using an adaptive Kalman filter is proposed. The LPV model is comprised of scheduling parameters, and the emulator parameters. The scheduling parameters are chosen such that they are capable of tracking variations in the system model as a result of changes in the operating regimes. The emulator parameters, on the other hand, simulate variations in the subsystems during the identification phase and have negligible effect during the operational phase. The nominal model and the influence vectors, which are the gradient of the feature vector respect to the emulator parameters, are identified off-line from a number of emulator parameter perturbed experiments. A Kalman filter is designed using the identified nominal model. As the system varies, the Kalman filter model is adapted using the scheduling variables. The residual is employed for fault diagnosis. The proposed scheme is successfully evaluated on simulated system as well as on a physical process control system.

Keywords: identification, linear parameter-varying systems, least-squares estimation, fault diagnosis, Kalman filter, emulators

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Authors: Sreedevi P. Chakyar, Jolly Andrews, V. P. Joseph

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A compact method for measuring the relative permittivity of a dielectric material at different temperatures using a single circular Split Ring Resonator (SRR) metamaterial unit working as a test probe is presented in this paper. The dielectric constant of a material is dependent upon its temperature and the LC resonance of the SRR depends on its dielectric environment. Hence, the temperature of the dielectric material in contact with the resonator influences its resonant frequency. A single SRR placed between transmitting and receiving probes connected to a Vector Network Analyser (VNA) is used as a test probe. The dependence of temperature between 30 ^oC and 60 ^oC on resonant frequency of SRR is analysed. Relative permittivities ‘ε’ of test samples for different temperatures are extracted from a calibration graph drawn between the relative permittivity of samples of known dielectric constant and their corresponding resonant frequencies. This method is found to be an easy and efficient technique for analysing the temperature dependent permittivity of different materials.

Keywords: metamaterials, negative permeability, permittivity measurement techniques, split ring resonators, temperature dependent dielectric constant

Procedia PDF Downloads 412

Authors: Gagnesh Kumar, Nitin Gupta

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Ternary Content Addressable Memory cells are mainly popular in network routers for packet forwarding and packet classification, but they are also useful in a variety of other applications that require high-speed table look-up. The main TCAM-design challenge is to decrease the power consumption associated with the large amount of parallel active circuitry, without compromising with speed or memory density. Furthermore, when the channel length decreases, leakage power becomes more significant, and it can even dominate dynamic power at lower technologies. In this paper, we propose a TCAM-design technique, called Virtual Power Supply technique that reduces the leakage by a substantial amount.

Keywords: match line (ML), search line (SL), ternary content addressable memory (TCAM), Leakage power (LP)

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Authors: Michel Kulhandjian, Dimitris A. Pados

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The objective of this work is to develop a theoretical framework for reliable digital recording system identification from digital audio files alone, for forensic purposes. A digital recording system consists of a microphone and a digital sound processing card. We view the cascade as a system of unknown transfer function. We expect same manufacturer and model microphone-sound card combinations to have very similar/near identical transfer functions, bar any unique manufacturing defect. Input voice (or other) signals are modeled as non-stationary processes. The technical problem under consideration becomes blind deconvolution with non-stationary inputs as it manifests itself in the specific application of digital audio recording equipment classification.

Keywords: blind system identification, audio fingerprinting, blind deconvolution, blind dereverberation

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Authors: Brian Romansky

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There are many practical, commercially available robots and semi-autonomous systems that are currently available for use in a wide variety of construction tasks. Adoption of these technologies has the potential to reduce the time and cost to deliver a project, reduce variability and risk in delivery time, increase quality, and improve safety on the job site. These benefits come with a cost for equipment rental or contract fees, access to specialists to configure the system, and time needed for set-up and support of the machines while in use. Calculation of the net ROI (Return on Investment) requires detailed information about the geometry of the site, the volume of work to be done, the overall project schedule, as well as data on the capabilities and past performance of available robotic systems. Assembling the required data and comparing the ROI for several options is complex and tedious. Many project managers will only consider the use of a robot in targeted applications where the benefits are obvious, resulting in low levels of adoption of automation in the construction industry. This work demonstrates how data already resident in many BIM (Building Information Model) projects can be used to automate ROI estimation for a sample set of commercially available construction robots. Calculations account for set-up and operating time along with scheduling support tasks required while the automated technology is in use. Configuration parameters allow for prioritization of time, cost, or safety as the primary benefit of the technology. A path toward integration and use of automatic ROI calculation with a database of available robots in a BIM platform is described.

Keywords: automation, BIM, robot, ROI.

Procedia PDF Downloads 88

Authors: Ali Siami, Masoud Asayesh, Asghar Najafi, Amirhosein Hamedanian

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There are so many noise sources in power generation units that these sources can produce high-level sound noise. Therefore, sound noise reduction methods can assist these industries, especially in these days that laws related to environmental issues become more strict. In a typical power plant, so many machines and devices with high-level sound noise are arranged beside of each others. Therefore, the sound source identification and reducing the noise level can be very vital. In this paper, the procedure for designing, manufacturing and testing of a silencer-muffler used for a power plant steam vent is mentioned. This unit is placed near the residential area and so it is very important to reduce the noise emission. For this purpose, in the first step, measurements have done to identify the sound source and the frequency content of noise. The overall level of noise was so high and it was more than 120dB. Then, the appropriate noise control device is designed according to the measurement results and operational conditions. In the next step, the designed silencer-muffler has been manufactured and installed on the steam discharge of the ejector. For validation of the silencer-muffler effect, the acoustic test was done again in operating mode. Finally, the measurement results before and after the installation are compared. The results have confirmed a considerable reduction in noise level resultant of using silencer-muffler in the designed frequency range.

Keywords: silencer-muffler, sound noise control, sound measurement, steam ejector

Procedia PDF Downloads 384

Authors: Jialin Wu, Zhiwei Lian, Jieyu Tang, Jingyun Shen

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Tornado is a serious and unpredictable natural disaster, which has an important impact on people's production and life. The probability of being hit by tornadoes in China was analyzed considering the principles of tornado formation. Then some suggestions on layout and shapes for newly-built buildings were provided combined with the characteristics of tornado wind fields. Fuzzy clustering and inverse closeness methods were used to evaluate the probability levels of tornado risks in various provinces based on classification and ranking. GIS was adopted to display the results. Finally, wind field single-vortex tornado was studied to discuss the optimized design of rural low-rise houses in Yancheng, Jiangsu as an example. This paper may provide enough data to support building and urban design in some specific regions.

Keywords: tornado probability, computational fluid dynamics, fuzzy mathematics, optimal design

Procedia PDF Downloads 136

Authors: A. Abdikian

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Propagation of nonlinear acoustic wave in dense electron-positron (e-p) plasmas in the presence of an external magnetic field and stationary ions (to neutralize the plasma background) is studied. By means of the quantum hydrodynamics model and applying the reductive perturbation method, the Zakharov-Kuznetsov equation is derived. Using the bifurcation theory of planar dynamical systems, the compressive structure of electrostatic solitary wave and periodic travelling waves is found. The numerical results show how the ion density ratio, the ion cyclotron frequency, and the direction cosines of the wave vector affect the nonlinear electrostatic travelling waves. The obtained results may be useful to better understand the obliquely nonlinear electrostatic travelling wave of small amplitude localized structures in dense magnetized quantum e-p plasmas and may be applicable to study the particle and energy transport mechanism in compact stars such as the interior of massive white dwarfs etc.

Keywords: bifurcation theory, phase portrait, magnetized electron-positron plasma, the Zakharov-Kuznetsov equation

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Authors: Girts Zageris, Vadims Geza, Andris Jakovics

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Gasification processes are of great interest due to their generation of renewable energy in the form of syngas from biodegradable waste. It is, therefore, important to study the factors that play a role in the efficiency of gasification and the longevity of the machines in which gasification takes place. This study focuses on the latter, aiming to optimize an entrained-flow gasifier by reducing slag formation on its walls to reduce maintenance costs. A CFD mathematical model for an entrained-flow gasifier is constructed – the model of an actual gasifier is rendered in 3D and appropriately meshed. Then, the turbulent gas flow in the gasifier is modeled with the realizable k-ε approach, taking devolatilization, combustion and coal gasification into account. Various such simulations are conducted, obtaining results for different air inlet positions and by tracking particles of varying sizes undergoing devolatilization and gasification. The model identifies potential problematic zones where most particles collide with the gasifier walls, indicating risk regions where ash deposits could most likely form. In conclusion, the effects on the formation of an ash layer of air inlet positioning and particle size allowed in the main gasifier tank are discussed, and possible solutions for decreasing a number of undesirable deposits are proposed. Additionally, an estimate of the impact of different factors such as temperature, gas properties and gas content, and different forces acting on the particles undergoing gasification is given.

Keywords: biomass particles, gasification, slag formation, turbulence k-ε modelling

Procedia PDF Downloads 286

Authors: Silvia Portarena, Chiara Anselmi, Chiara Baldacchini, Enrico Brugnoli

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Extra virgin olive oil (EVOO) is a complex matrix mainly composed by fatty acid and other minor compounds, among which carotenoids are well known for their antioxidative function that is a key mechanism of protection against cancer, cardiovascular diseases, and macular degeneration in humans. EVOO composition in terms of such constituents is generally the result of a complex combination of genetic, agronomical and environmental factors. To selectively improve the quality of EVOOs, the role of each factor on its biochemical composition need to be investigated. By selecting fruits from four different cultivars similarly grown and harvested, it was demonstrated that Raman spectroscopy, combined with chemometric analysis, is able to discriminate the different cultivars, also as a function of the harvest date, based on the relative content and composition of fatty acid and carotenoids. In particular, a correct classification up to 94.4% of samples, according to the cultivar and the maturation stage, was obtained. Moreover, by using gas chromatography and high-performance liquid chromatography as reference techniques, the Raman spectral features further allowed to build models, based on partial least squares regression, that were able to predict the relative amount of the main fatty acids and the main carotenoids in EVOO, with high coefficients of determination. Besides genetic factors, climatic parameters, such as light exposition, distance from the sea, temperature, and amount of precipitations could have a strong influence on EVOO composition of both major and minor compounds. This suggests that the Raman spectra could act as a specific fingerprint for the geographical discrimination and authentication of EVOO. To understand the influence of environment on EVOO Raman spectra, samples from seven regions along the Italian coasts were selected and analyzed. In particular, it was used a dual approach combining Raman spectroscopy and isotope ratio mass spectrometry (IRMS) with principal component and linear discriminant analysis. A correct classification of 82% EVOO based on their regional geographical origin was obtained. Raman spectra were obtained by Super Labram spectrometer equipped with an Argon laser (514.5 nm wavelenght). Analyses of stable isotope content ratio were performed using an isotope ratio mass spectrometer connected to an elemental analyzer and to a pyrolysis system. These studies demonstrate that RR spectroscopy is a valuable and useful technique for the analysis of EVOO. In combination with statistical analysis, it makes possible the assessment of specific samples’ content and allows for classifying oils according to their geographical and varietal origin.

Keywords: authentication, chemometrics, olive oil, raman spectroscopy

Procedia PDF Downloads 332

Authors: Abhijith Asok

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This paper presents a generalized approach for the simple linear data transformation, Y=bX, through an integration of multidimensional coordinate geometry, vector space theory and polygonal geometry. The scaling is performed by adding an additional ’Dummy Dimension’ to the n-dimensional data, which helps plot two dimensional component-wise straight lines on pairs of dimensions. The end result is a set of scaled extensions of observations in any of the 2n spatial divisions, where n is the total number of applicable dimensions/dataset variables, created by shifting the n-dimensional plane along the ’Dummy Axis’. The derived scaling factor was found to be dependent on the coordinates of the common point of origin for diverging straight lines and the plane of extension, chosen on and perpendicular to the ’Dummy Axis’, respectively. This result indicates the geometrical interpretation of a linear data transformation and hence, opportunities for a more informed choice of the factor ’b’, based on a better choice of these coordinate values. The paper follows on to identify the effect of this transformation on certain popular distance metrics, wherein for many, the distance metric retained the same scaling factor as that of the features.

Keywords: data transformation, dummy dimension, linear transformation, scaling

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Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

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Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

Procedia PDF Downloads 223

Authors: Divya Agarwal, Pushpendra S. Bharti

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Autonomous mobile robots (AMR) are expected as smart tools for operations in every automation industry. Path planning and obstacle avoidance is the backbone of AMR as robots have to reach their goal location avoiding obstacles while traversing through optimized path defined according to some criteria such as distance, time or energy. Path planning can be classified into global and local path planning where environmental information is known and unknown/partially known, respectively. A number of sensors are used for data collection. A number of algorithms such as artificial potential field (APF), rapidly exploring random trees (RRT), bidirectional RRT, Fuzzy approach, Purepursuit, A* algorithm, vector field histogram (VFH) and modified local path planning algorithm, etc. have been used in the last three decades for path planning and obstacle avoidance for AMR. This paper makes an attempt to review some of the path planning and obstacle avoidance algorithms used in the field of AMR. The review includes comparative analysis of simulation and mathematical computations of path planning and obstacle avoidance algorithms using MATLAB 2018a. From the review, it could be concluded that different algorithms may complete the same task (i.e. with a different set of instructions) in less or more time, space, effort, etc.

Keywords: path planning, obstacle avoidance, autonomous mobile robots, algorithms

Procedia PDF Downloads 232

Authors: Kaveh Shahi, Helmi Z. M. Shafri, Ebrahim Taherzadeh

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In remote sensing, shadow causes problems in many applications such as change detection and classification. It is caused by objects which are elevated, thus can directly affect the accuracy of information. For these reasons, it is very important to detect shadows particularly in urban high spatial resolution imagery which created a significant problem. This paper focuses on automatic shadow detection based on a new spectral index for multispectral imagery known as Shadow Detection Index (SDI). The new spectral index was tested on different areas of World-View 2 images and the results demonstrated that the new spectral index has a massive potential to extract shadows effectively and automatically.

Keywords: spectral index, shadow detection, remote sensing images, World-View 2

Procedia PDF Downloads 538

Authors: Abduselam Faris, Rijalu Negash, Zera Kedir

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This study was initiated to identify constraints and opportunities of the wood production value chain in Southwest Ethiopia. About 385 wood trees growing farmers were randomly interviewed. Similarly, about 30 small-scale wood processors, 30 retailers, 15 local collectors and 5 wholesalers were purposively included in the study. The results of the study indicated that 98.96 % of the smallholder farmers that engaged in the production of wood trees which is used for wood were male-headed, with an average age of 46.88 years. The main activity that the household engaged was agriculture (crop and livestock) which accounts for about 61.56% of the sample respondents. Through value chain mapping of actors, the major value chain participant and supporting actors were identified. On average, the tree-growing farmers generated gross income of 9385.926 Ethiopian birr during the survey year. Among the critical constraints identified along the wood production value chain was limited supply of credit, poor market information dissemination, high interference of brokers, and shortage of machines, inadequate working area and electricity. The availability of forest resources is the leading opportunity in the wood production value chain. Reinforcing the linkage among wood production value chain actors, providing skill training for small-scale processors, and developing suitable policy for wood tree wise use is key recommendations forward.

Keywords: value chain analysis, wood production, southwest Ethiopia, constraints and opportunities

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Authors: Niklas Panten, Eberhard Abele

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This paper presents a novel approach for real-time and near-optimal control of industrial smart grids by deep reinforcement learning (DRL). To achieve highly energy-efficient factory systems, the energetic linkage of machines, technical building equipment and the building itself is desirable. However, the increased complexity of the interacting sub-systems, multiple time-variant target values and stochastic influences by the production environment, weather and energy markets make it difficult to efficiently control the energy production, storage and consumption in the hybrid industrial smart grids. The studied deep reinforcement learning approach allows to explore the solution space for proper control policies which minimize a cost function. The deep neural network of the DRL agent is based on a multilayer perceptron (MLP), Long Short-Term Memory (LSTM) and convolutional layers. The agent is trained within multiple Modelica-based factory simulation environments by the Advantage Actor Critic algorithm (A2C). The DRL controller is evaluated by means of the simulation and then compared to a conventional, rule-based approach. Finally, the results indicate that the DRL approach is able to improve the control performance and significantly reduce energy respectively operating costs of industrial smart grids.

Keywords: industrial smart grids, energy efficiency, deep reinforcement learning, optimal control

Procedia PDF Downloads 195

Authors: Carlos Huertas, Reyes Juarez-Ramirez

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Public health is one of the most critical issues today; therefore, there is great interest to improve technologies in the area of diseases detection. With machine learning and feature selection, it has been possible to aid the diagnosis of several diseases such as cancer. In this work, we present an extension to the Heat Map Based Feature Selection algorithm, this modification allows automatic threshold parameter selection that helps to improve the generalization performance of high dimensional data such as mass spectrometry. We have performed a comparison analysis using multiple cancer datasets and compare against the well known Recursive Feature Elimination algorithm and our original proposal, the results show improved classification performance that is very competitive against current techniques.

Keywords: biomarker discovery, cancer, feature selection, mass spectrometry

Procedia PDF Downloads 338

Authors: C. M. Priesnitz, G. Zanandrea, J. P. Fabris, S. L. Russo, M. E. Camargo

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This study aimed to the prospection the physiotherapy area technological production registered with the National Intellectual Property Institute (INPI) in Brazil, for understand the evolution of the technological production in the country over time and visualize the distribution this production request in Brazil. There was an evolution in the technology landscape, where the average annual deposits had an increase of 102%, from 3.14 before the year 2004 to 6,33 after this date. It was found differences in the distribution of the number the deposits requested to each Brazilian region, being that of the 132 request, 68,9% were from the southeast region. The international patent classification evaluated the request deposits, and the more found numbers were A61H and A63B. So even with an improved panorama of technology production, this should still have incentives since it is an important tool for the development of the country.

Keywords: distribution, evolution, patent, physiotherapy, technological prospecting

Procedia PDF Downloads 329

Authors: Deepika Christopher, Garima Anand

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To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

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Authors: Parameshwaran Nair

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Filling and packaging lines for bottling of beverages into glass, PET or aluminum containers require specialized expertise and a different configuration of equipment like – Filler, Warmer, Labeller, Crater/Recrater, Shrink Packer, Carton Erector, Carton Sealer, Date Coder, Palletizer, etc. Over the period of time, the packaging industry has evolved from manually operated single station machines to highly automized high-speed lines. Human factor and ergonomics have gained significant consideration in this course of transformation. A pre-requisite for such bottling lines, irrespective of the container type and size, is to be suitable for multi-format applications. It should also be able to handle format changeovers with minimal adjustment. It should have variable capacity and speeds, for providing great flexibility of use in managing accumulation times as a function of production characteristics. In terms of layout as well, it should demonstrate flexibility for operator movement and access to machine areas for maintenance. Packaging technology during the past few decades has risen to these challenges by a series of major breakthroughs interspersed with periods of refinement and improvement. The milestones are many and varied and are described briefly in this paper. In order to have a brief understanding of the human factor and ergonomics in the modern packaging lines, this paper, highlights the various technologies, design considerations and statutory requirements in packaging equipment for different types of containers used in India.

Keywords: human-factor, ergonomics, bottling lines, automized high-speed lines

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Authors: Kehinde Damilola Ilesanmi, Dev Datt Tewari

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South Africa is in its post-industrial era moving from the primary and secondary sector to the tertiary sector. The study investigated the impact of the disaggregated energy consumption (coal, oil, and electricity) on the primary, secondary and tertiary sectors of the economy between 1980 and 2012 in South Africa. Using vector error correction model, it was established that South Africa is an energy dependent economy, and that energy (especially electricity and oil) is a limiting factor of growth. This implies that implementation of energy conservation policies may hamper economic growth. Output growth is significantly outpacing energy supply, which has necessitated load shedding. To meet up the excess energy demand, there is a need to increase the generating capacity which will necessitate increased investment in the electricity sector as well as strategic steps to increase oil production. There is also need to explore more renewable energy sources, in order to meet the growing energy demand without compromising growth and environmental sustainability. Policy makers should also pursue energy efficiency policies especially at sectoral level of the economy.

Keywords: causality, economic growth, energy consumption, hypothesis, sectoral output

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Authors: Angel Pérez Sánchez

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Background: Consider magnetic lines of force as a vector magnetic current was introduced by convention around 1830. But this leads to a dead end in traditional physics, and quantum explanations must be referred to explain the magnetic phenomenon. However, a study of magnetic lines as percussive waves leads to other paths capable of interpreting magnetism through traditional physics. Methodology: Brick used in the experiment: two parallel electric current cables attract each other if current goes in the same direction and its application at a microscopic level inside magnets. Significance: Consideration of magnetic lines as magnets themselves would mean a paradigm shift in the study of magnetism and open the way to provide solutions to mysteries of magnetism until now only revealed by quantum mechanics. Major findings: discover how a magnetic field is created, as well as reason how magnetic attraction and repulsion work, understand how magnets behave when splitting them, and reveal the impossibility of a Magnetic Monopole. All of this is presented as if it were a symphony in which all the notes fit together perfectly to create a beautiful, smart, and simple work.

Keywords: magnetic lines of force, magnetic field, magnetic attraction and repulsion, magnet split, magnetic monopole, magnetic lines of force as magnets, magnetic lines of force as waves

Procedia PDF Downloads 90

Authors: Jiangbo Li, Wenqian Huang

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Early detection of fungal infection in citrus fruit is one of the major problems in the postharvest commercialization process. The automatic and nondestructive detection of infected fruits is still a challenge for the citrus industry. At present, the visual inspection of rotten citrus fruits is commonly performed by workers through the ultraviolet induction fluorescence technology or manual sorting in citrus packinghouses to remove fruit subject with fungal infection. However, the former entails a number of problems because exposing people to this kind of lighting is potentially hazardous to human health, and the latter is very inefficient. Orange is used as a research object. This study would focus on this problem and proposed an effective method based on Vis-NIR hyperspectral imaging in the wavelength range of 400-1000 nm with a spectroscopic resolution of 2.8 nm. In this work, three normalization approaches are applied prior to analysis to reduce the effect of sample curvature on spectral profiles, and it is found that mean normalization was the most effective pretreatment for decreasing spectral variability due to curvature. Then, principal component analysis (PCA) was applied to a dataset composing of average spectra from decayed and normal tissue to reduce the dimensionality of data and observe the ability of Vis-NIR hyper-spectra to discriminate data from two classes. In this case, it was observed that normal and decayed spectra were separable along the resultant first principal component (PC1) axis. Subsequently, five wavelengths (band) centered at 577, 702, 751, 808, and 923 nm were selected as the characteristic wavelengths by analyzing the loadings of PC1. A multispectral combination image was generated based on five selected characteristic wavelength images. Based on the obtained multispectral combination image, the intensity slicing pseudocolor image processing method is used to generate a 2-D visual classification image that would enhance the contrast between normal and decayed tissue. Finally, an image segmentation algorithm for detection of decayed fruit was developed based on the pseudocolor image coupled with a simple thresholding method. For the investigated 238 independent set samples including infected fruits infected by Penicillium digitatum and normal fruits, the total success rate is 100% and 97.5%, respectively, and, the proposed algorithm also used to identify the orange infected by penicillium italicum with a 100% identification accuracy, indicating that the proposed multispectral algorithm here is an effective method and it is potential to be applied in citrus industry.

Keywords: citrus fruit, early rotten, fungal infection, hyperspectral imaging

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Authors: Tianyang Xu

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Small underwater targets generally have a non-centrosymmetric geometry, and the acoustic scattering field of the target has spatial inhomogeneity under active sonar detection conditions. In view of the above problems, this paper takes the hemispherical cylindrical shell as the research object, and considers the angle continuity implied in the echo characteristics, and proposes a cluster-driven research method for the non-uniform characteristics of target echo angle. First, the target echo features are extracted, and feature vectors are constructed. Secondly, the t-SNE algorithm is used to improve the internal connection of the feature vector in the low-dimensional feature space and to construct the visual feature space. Finally, the implicit angular relationship between echo features is extracted under unsupervised condition by cluster analysis. The reconstruction results of the local geometric structure of the target corresponding to different categories show that the method can effectively divide the angle interval of the local structure of the target according to the natural acoustic scattering characteristics of the target.

Keywords: underwater target;, non-uniform characteristics;, cluster-driven method;, acoustic scattering characteristics

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Authors: T. N. Goh, M. Hashmi, O. Hussain

Abstract:

Temporomandibular joint (TMJ) dysfunction (TMD) is a condition that may affect patients via restricted mouth opening, significant pain during normal functioning, and/or reproducible joint noise. TMD includes myofascial pain, TMJ functional derangements (internal derangement, dislocation), and TMJ degenerative/inflammatory joint disease. Internal derangement (ID) is the most common cause of TMD-related clicking and locking. These patients are managed in a stepwise approach, from patient education (homecare advice and analgesia), splint therapy, physiotherapy, botulinum toxin treatment, to arthrocentesis. Arthrotomy is offered when the aforementioned treatment options fail to alleviate symptoms and improve quality of life. The aim of this prospective study was to review the outcomes of jaw joint open surgery in TMD patients. Patients who presented from 2015-2022 at the Oral and Maxillofacial Surgery Department in the Doncaster NHS Foundation Trust, UK, with a Wilkes classification of III -V were included. These patients underwent either i) discopexy with bone-anchoring suture (9); ii) intrapositional temporalis flap (ITF) with bone-anchoring suture (3); iii) eminoplasty and discopexy with suturing to the capsule (3); iii) discectomy + ITF with bone-anchoring suture (1); iv) discoplasty + bone-anchoring suture (1); v) ITF (1). Maximum incisal opening (MIO) was assessed pre-operatively and at each follow-up. Pain score, determined via the visual analogue scale (VAS, with 0 being no pain and 10 being the worst pain), was also recorded. A total of 18 eligible patients were identified with a mean age of 45 (range 22 - 79), of which 16 were female. The patients were scored by Wilkes Classification as III (14), IV (1), or V (4). Twelve patients had anterior disc displacement without reduction (66%) and six had degenerative/arthritic changes (33%) to the TMJ. The open joint procedure resulted in an increase in MIO and reduction in pain VAS and for the majority of patients, across all Wilkes Classifications. Pre-procedural MIO was 22.9 ± 7.4 mm and VAS was 7.8 ± 1.5. At three months post-procedure there was an increase in MIO to 34.4 ± 10.4 mm (p < 0.01) and a decrease in the VAS to 1.5 ± 2.9 (p < 0.01). Three patients were lost to follow-up prior to six months. Six were discharged at six month review and five patients were discharged at 12 months review as they were asymptomatic with good mouth opening. Four patients are still attending for annual botulinum toxin treatment. Two patients (Wilkes III and V) subsequently underwent TMJ replacement (11%). One of these patients (Wilkes III) had improvement initially to MIO of 40 mm, but subsequently relapsed to less than 20 mm due to lack of compliance with jaw rehabilitation device post-operatively. Clinical improvements in 89% of patients within the study group were found, with a return to near normal MIO range and reduced pain score. Intraoperatively, the operator found bone-anchoring suture used for discopexy/discoplasty more secure than the soft tissue anchoring suturing technique.

Keywords: bone anchoring suture, open temporomandibular joint surgery, temporomandibular joint, temporomandibular joint dysfunction

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Authors: Hollie M. Lewis

Abstract:

A large body of corroborating research provides evidence that traditional undergraduate engineering education fails to provide students with a role and identity that requires social concern and moral reasoning. Engineering students demonstrate a low level of engagement with social and political contexts, which further declines over the course of engineering education. This detachment is thought to stem from beliefs that the role of the engineer is purely to design machines, systems, and structures. In effect, engineers objectify the world. The purpose of this paper is to provide an ecofeminist critique of engineering education and pose the benefits of social justice pedagogies incorporating ecofeminist discourse. The challenges currently facing the world stem from anthropocentric industrialization, an agenda that is historically absent of Environmental, Feminist, People of Color, and Indigenous voices. A future in which the global collective achieves its Sustainable Development Goals requires its engineers to have a solid understanding of the broader social and political contexts in which they manage projects. Engineering education must convey the influence of the professional role of engineer and encourage the practice of critical reflection and social perspective-taking, priming students with the skills to engage with varying perspectives and discourses. There will be discussed the facets of social justice pedagogies that aid students in surpassing threshold concepts in social justice.

Keywords: feminism in engineering, sustainable development, engineering education, social justice pedagogies

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Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

Abstract:

Document Analysis is an important research field that aims to gather the information by analyzing the data in documents. As one of the important targets for many fields is to understand what people actually want, sentimental analysis field has been one of the vital fields that are tightly related to the document analysis. This research focuses on analyzing text documents to classify each document according to its opinion. The aim of this research is to detect the emotions from text documents based on enriching the lexicon with adapting their content based on semantic patterns extraction. The proposed approach has been presented, and different experiments are applied by different perspectives to reveal the positive impact of the proposed approach on the classification results.

Keywords: document analysis, sentimental analysis, emotion detection, WEKA tool, NRC lexicon

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Authors: Ahmed Abdallatif

Abstract:

Background: Patients with malignant germ cell tumors have age distribution in two peaks, with the first one during infancy and the second after the onset of puberty. Gonadal germ cell tumors are the most common malignant ovarian tumor in females aged below twenty years. Sacrococcygeal and retroperitoneal abdominal tumors usually presents in a large size before the onset of symptoms. Methods: Patients with pediatric germ cell tumors presenting to Children’s Cancer Hospital Egypt and National Cancer Institute Egypt from January 2008 to June 2011 Patients underwent stratification according to risk into low, intermediate and high risk groups according to children oncology group classification. Objectives: Assessment of the clinicopathologic features of all cases of pediatric germ cell tumors and classification of malignant cases according to their stage, and the primary site to low, intermediate and high risk patients. Evaluation of surgical management in each group of patients focusing on surgical approach, the extent of surgical resection according to each site, ability to achieve complete surgical resection and perioperative complications. Finally, determination of the three years overall and disease-free survival in different groups and the relation to different prognostic factors including the extent of surgical resection. Results: Out of 131 cases surgically explored only 26 cases had re exploration with 8 cases explored for residual disease 9 cases for remote recurrence or metastatic disease and the other 9 cases for other complications. Patients with low risk kept under follow up after surgery, out of those of low risk group (48 patients) only 8 patients (16.5%) shifted to intermediate risk. There were 20 patients (14.6%) diagnosed as intermediate risk received 3 cycles of compressed (Cisplatin, Etoposide and Bleomycin) and all high risk group patients 69patients (50.4%) received chemotherapy. Stage of disease was strongly and significantly related to overall survival with a poorer survival in late stages (stage IV) as compared to earlier stages. Conclusion: Overall survival rate at 3 three years was (76.7% ± 5.4, 3) years EFS was (77.8 % ±4.0), however 3 years DFS was much better (89.8 ± 3.4) in whole study group with ovarian tumors had significantly higher Overall survival (90% ± 5.1). Event Free Survival analysis showed that Male gender was 3 times likely to have bad events than females. Patients who underwent incomplete resection were 4 times more than patients with complete resection to have bad events. Disease free survival analysis showed that Patients who underwent incomplete surgery were 18.8 times liable for recurrence compared to those who underwent complete surgery, and patients who were exposed to re-excision were 21 times more prone to recurrence compared to other patients.

Keywords: extragonadal, germ cell tumors, gonadal, pediatric

Procedia PDF Downloads 218